As machine learning (ML) achieves remarkable success in a wide range of areas, there is a growing need to show real life robustness of ML models through cross-dataset generalizability.
Various domain generalization techniques have been proposed to improve model performance when the probability distributions of training data and testing data are different [1]}, [2]}.
The majority of existing domain generalization algorithms focus on the tasks of computer vision (CV) [3]}, [4]}, [5]}, [6]} and natural language processing (NLP) [7]}, [8]}, [9]}, [10]}.
Only a few studies have examined domain generalization on time-series data [11]}, [12]}, [13]}, other than short-term human action recognition [14]}, [15]}.
However, even this prior research has only investigated time-series data in controlled settings [16]} and did not explore domain generalization in longitudinal time-series sensor data in the wild.
To build deployable longitudinal time-series systems, it is important to evaluate the model across datasets with different contexts to ensure its generalizability for real-world applications, such as health monitoring [17]}, medical analysis [18]}, personalized recommendation [19]}, and weather prediction [20]}.
